Classification Method of Heavy Oil Based on Chemical Composition and Bulk Properties

Weilai Zhang, Jianxun Wu (), Shuofan Li, Yahe Zhang, Suoqi Zhao and Quan Shi ()
Additional contact information
Weilai Zhang: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China
Jianxun Wu: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China
Shuofan Li: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China
Yahe Zhang: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China
Suoqi Zhao: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China
Quan Shi: State Key Laboratory of Heavy Oil Processing, Petroleum Molecular Engineering Center (PMEC), China University of Petroleum, Beijing 102249, China

Energies, 2024, vol. 17, issue 15, 1-11

Abstract: Heavy oil resources in the world are extremely abundant, and viscosity is currently the main reference index for heavy oil classification. However, the diversification of practical issues in heavy oil exploitation, and the refinement of processing and utilization urgently require the support of heavy oil classification with more reference indexes. In this study, the macroscopic properties of typical heavy oils in China were analyzed, and the semi-quantitative analysis of the molecular composition of different heavy oils was completed based on high-resolution mass spectrometry. The results show that heavy oils with similar viscosities can exhibit huge differences in macroscopic properties and chemical composition. According to the evaluation of the chemical composition and macroscopic properties of typical Chinese heavy oils, 12 types of compounds belonging to saturates, aromatics, resins, and asphaltenes (SARA) were identified, establishing a connection between the macroscopic fractions and molecular compositions of heavy oils. By summarizing the comparative results, a new classification criterion for heavy oils was established, focusing on the main parameters of H/C ratio and total acid number (TAN), with sulfur content as a supplementary indicator. H/C is the embodiment of the degree of molecular condensation in the macroscopic properties, reflecting the structural characteristics of the main molecules of the heavy oil. Chinese heavy oil is generally characterized by high TAN, which corresponds to the composition of petroleum acids, and it is also an important reference index for the exploitation and processing of heavy oils. Most Chinese heavy oils have a very low sulfur content, but the presence of sulfur compounds in high-sulfur heavy oils can lead to significant differences in the distribution of compound types among the SARA. This new classification method for heavy oil combines the characteristics of chemical composition of heavy oils, which is expected to provide valuable support for the extraction and processing of heavy oil.

Keywords: heavy oil; bulk properties; chemical composition; classification; high-resolution mass spectrometry (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/15/3733/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/15/3733/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:15:p:3733-:d:1445039

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().